{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_ner_ONTO_18class_example.ipynb","provenance":[{"file_id":"1CYzHfQyFCdvIOVO2Z5aggVI9c0hDEOrw","timestamp":1599267946314}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"NYQRU3pRO146"},"source":["![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n","\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/tutorial_docs/examples/colab/component_examples/named_entity_recognition_NER/NLU_ner_ONTO_18class_example.ipynb)\n","\n","# Named-entity recognition with Deep Learning ONTO NOTES\n","\n","Named-Entity recognition is a well-known technique in information extraction it is also known as entity identification, entity chunking and entity extraction.   Knowing the relevant tags for each article help in automatically categorizing the articles in defined hierarchies and enable smooth content discovery. This pipeline is based on NerDLApproach annotator with Char CNN - BiLSTM and GloVe Embeddings on the OntoNotes corpus and supports the identification of 18 entities.\n","\n","\n","Following NER classes can be detected by this model\n","\n","\n","\n","\n","|Type | \tDescription |\n","|------|--------------|\n","| PERSON | \tPeople, including fictional like **Harry Potter** |\n","| NORP | \tNationalities or religious or political groups like the **Germans** |\n","| FAC | \tBuildings, airports, highways, bridges, etc. like **New York Airport** |\n","| ORG | \tCompanies, agencies, institutions, etc. like **Microsoft** |\n","| GPE | \tCountries, cities, states. like **Germany** |\n","| LOC | \tNon-GPE locations, mountain ranges, bodies of water. Like the **Sahara desert**|\n","| PRODUCT | \tObjects, vehicles, foods, etc. (Not services.) like **playstation** |\n","| EVENT | \tNamed hurricanes, battles, wars, sports events, etc. like **hurricane Katrina**|\n","| WORK_OF_ART | \tTitles of books, songs, etc. Like **Mona Lisa** |\n","| LAW | \tNamed documents made into laws. Like : **Declaration of Independence** |\n","| LANGUAGE | \tAny named language. Like **Turkish**|\n","| DATE | \tAbsolute or relative dates or periods. Like every second **friday**|\n","| TIME | \tTimes smaller than a day. Like **every minute**|\n","| PERCENT | \tPercentage, including ”%“. Like **55%** of workers enjoy their work |\n","| MONEY | \tMonetary values, including unit. Like **50$** for those pants |\n","| QUANTITY | \tMeasurements, as of weight or distance. Like this person weights **50kg** |\n","| ORDINAL | \t“first”, “second”, etc. Like David placed **first** in the tournament |\n","| CARDINAL | \tNumerals that do not fall under another type. Like **hundreds** of models are avaiable in NLU |"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1649992907248,"user_tz":-300,"elapsed":103752,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"4a51f85a-cc23-420d-90f2-1063d9cefe21"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","  \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-04-15 03:20:02--  https://setup.johnsnowlabs.com/nlu/colab.sh\n","Resolving setup.johnsnowlabs.com (setup.johnsnowlabs.com)... 51.158.130.125\n","Connecting to setup.johnsnowlabs.com (setup.johnsnowlabs.com)|51.158.130.125|:443... connected.\n","HTTP request sent, awaiting response... 302 Moved Temporarily\n","Location: https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh [following]\n","--2022-04-15 03:20:02--  https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1665 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","-                   100%[===================>]   1.63K  --.-KB/s    in 0s      \n","\n","2022-04-15 03:20:02 (34.6 MB/s) - written to stdout [1665/1665]\n","\n","Installing  NLU 3.4.3rc2 with  PySpark 3.0.3 and Spark NLP 3.4.2 for Google Colab ...\n","Ign:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n","Ign:2 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n","Get:3 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n","Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release [696 B]\n","Hit:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n","Get:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n","Get:7 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n","Hit:8 http://archive.ubuntu.com/ubuntu bionic InRelease\n","Get:9 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n","Get:10 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n","Hit:12 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n","Get:13 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [953 kB]\n","Get:14 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease [15.9 kB]\n","Get:15 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n","Hit:16 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n","Get:17 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,947 kB]\n","Get:18 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,490 kB]\n","Get:19 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,268 kB]\n","Get:20 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,695 kB]\n","Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [3,134 kB]\n","Get:22 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [996 kB]\n","Get:23 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic/main amd64 Packages [45.3 kB]\n","Fetched 13.8 MB in 4s (3,146 kB/s)\n","Reading package lists... Done\n","tar: spark-3.0.2-bin-hadoop2.7.tgz: Cannot open: No such file or directory\n","tar: Error is not recoverable: exiting now\n","\u001b[K     |████████████████████████████████| 209.1 MB 56 kB/s \n","\u001b[K     |████████████████████████████████| 142 kB 62.6 MB/s \n","\u001b[K     |████████████████████████████████| 505 kB 58.0 MB/s \n","\u001b[K     |████████████████████████████████| 198 kB 57.5 MB/s \n","\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting nlu_tmp==3.4.3rc10\n","  Downloading nlu_tmp-3.4.3rc10-py3-none-any.whl (510 kB)\n","\u001b[K     |████████████████████████████████| 510 kB 7.6 MB/s \n","\u001b[?25hRequirement already satisfied: pyarrow>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (6.0.1)\n","Requirement already satisfied: dataclasses in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (0.6)\n","Requirement already satisfied: pandas>=1.3.5 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (1.3.5)\n","Requirement already satisfied: spark-nlp<3.5.0,>=3.4.2 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (3.4.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (1.21.5)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.3.5->nlu_tmp==3.4.3rc10) (2018.9)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.3.5->nlu_tmp==3.4.3rc10) (2.8.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.3.5->nlu_tmp==3.4.3rc10) (1.15.0)\n","Installing collected packages: nlu-tmp\n","Successfully installed nlu-tmp-3.4.3rc10\n"]}]},{"cell_type":"markdown","metadata":{"id":"Gph8XOL1Pzpl"},"source":["# NLU makes NER easy. \n","\n","You just need to load the NER model via ner.load() and predict on some dataset.    \n","It could be a pandas dataframe with a column named text or just an array of strings."]},{"cell_type":"code","metadata":{"id":"pmpZSNvGlyZQ","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1649992974918,"user_tz":-300,"elapsed":67698,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"1e08e9a9-209a-4467-9042-ff5af1ee26f5"},"source":["import nlu \n","\n","example_text =  ['People, including fictional like Harry Potter.',\n","'Nationalities or religious or political groups like Germans.',\n","'Buildings, airports, highways, bridges, etc. like New York Airport',\n","'Companies, agencies, institutions, etc. like Microsoft',\n","'Countries, cities, states. like Germany',\n","'Non-GPE locations, mountain ranges, bodies of water. Like Sahara Destert',\n","'Objects, vehicles, foods, etc. (Not services.) Like the a  or playstation or Playstation',\n","'Named hurricanes, battles, wars, sports events, etc. like hurricane Katrina',\n","'Titles of books, songs, etc. Like the Mona Lisa',\n","'Named documents made into laws. Like the Declaration of Independence',\n","'Any named language. Like English',\n","'Absolute or relative dates or periods. Like every second friday',\n","'Times smaller than a day. Like every minute',\n","'Percentage, including ”%“. Like 55% of workers enjoy their work',\n","'Monetary values, including unit. Like 50$ for those pants',\n","'Measurements, as of weight or distance. Like this person weights 50kg',\n","'“first”, “second”, etc. Like David place first in the tournament',\n","'Numerals that do not fall under another type. Like hundreds of models are avaiable in NLU',]\n","nlu.load('ner.onto').predict(example_text)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["onto_recognize_entities_sm download started this may take some time.\n","Approx size to download 160.1 MB\n","[OK!]\n"]},{"output_type":"execute_result","data":{"text/plain":["                                             document      entities_onto  \\\n","0      People, including fictional like Harry Potter.      Harry Potter.   \n","1   Nationalities or religious or political groups...                NaN   \n","2   Buildings, airports, highways, bridges, etc. l...   New York Airport   \n","3   Companies, agencies, institutions, etc. like M...          Microsoft   \n","4             Countries, cities, states. like Germany            Germany   \n","5   Non-GPE locations, mountain ranges, bodies of ...            Non-GPE   \n","5   Non-GPE locations, mountain ranges, bodies of ...     Sahara Destert   \n","6   Objects, vehicles, foods, etc. (Not services.)...        Playstation   \n","7   Named hurricanes, battles, wars, sports events...  hurricane Katrina   \n","8     Titles of books, songs, etc. Like the Mona Lisa               Lisa   \n","9   Named documents made into laws. Like the Decla...                NaN   \n","10                   Any named language. Like English            English   \n","11  Absolute or relative dates or periods. Like ev...             second   \n","12        Times smaller than a day. Like every minute                NaN   \n","13  Percentage, including ”%“. Like 55% of workers...                55%   \n","14  Monetary values, including unit. Like 50$ for ...                50$   \n","15  Measurements, as of weight or distance. Like t...               50kg   \n","16  “first”, “second”, etc. Like David place first...              David   \n","16  “first”, “second”, etc. Like David place first...              first   \n","17  Numerals that do not fall under another type. ...           hundreds   \n","17  Numerals that do not fall under another type. ...                NLU   \n","\n","   entities_onto_class entities_onto_confidence  \\\n","0               PERSON                   0.3747   \n","1                  NaN                      NaN   \n","2                  FAC               0.49666667   \n","3                  ORG                    0.946   \n","4                  GPE                   0.9813   \n","5                  ORG                   0.9655   \n","5                  LOC                   0.5385   \n","6              PRODUCT                   0.9356   \n","7                EVENT                   0.7472   \n","8               PERSON                   0.6172   \n","9                  NaN                      NaN   \n","10            LANGUAGE                   0.9894   \n","11             ORDINAL                   0.2531   \n","12                 NaN                      NaN   \n","13             PERCENT                  0.84935   \n","14               MONEY                  0.74255   \n","15              PERSON                   0.2981   \n","16              PERSON                   0.9373   \n","16             ORDINAL                   0.9203   \n","17            CARDINAL                   0.9277   \n","17                 ORG                   0.9371   \n","\n","                                  word_embedding_onto  \n","0   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","1   [[-0.02076599933207035, 0.5784800052642822, 0....  \n","2   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","3   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","4   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","5   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","5   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","6   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","7   [[-0.3515700101852417, -0.1662600040435791, 0....  \n","8   [[0.5689799785614014, -0.38422998785972595, 0....  \n","9   [[-0.3515700101852417, -0.1662600040435791, 0....  \n","10  [[-0.2367600053548813, 0.15658999979496002, 0....  \n","11  [[-0.0853630006313324, -0.5337499976158142, 1....  \n","12  [[-0.29739999771118164, 0.1302099972963333, 0....  \n","13  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","14  [[0.3520300090312958, -0.1374099999666214, 0.2...  \n","15  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","16  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","16  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","17  [[-0.2671700119972229, 0.7479100227355957, -0....  \n","17  [[-0.2671700119972229, 0.7479100227355957, -0....  "],"text/html":["\n","  <div id=\"df-ed21a6d5-9147-49cb-9ec3-974539119971\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>entities_onto</th>\n","      <th>entities_onto_class</th>\n","      <th>entities_onto_confidence</th>\n","      <th>word_embedding_onto</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>People, including fictional like Harry Potter.</td>\n","      <td>Harry Potter.</td>\n","      <td>PERSON</td>\n","      <td>0.3747</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Nationalities or religious or political groups...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[-0.02076599933207035, 0.5784800052642822, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Buildings, airports, highways, bridges, etc. l...</td>\n","      <td>New York Airport</td>\n","      <td>FAC</td>\n","      <td>0.49666667</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Companies, agencies, institutions, etc. like M...</td>\n","      <td>Microsoft</td>\n","      <td>ORG</td>\n","      <td>0.946</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Countries, cities, states. like Germany</td>\n","      <td>Germany</td>\n","      <td>GPE</td>\n","      <td>0.9813</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Non-GPE locations, mountain ranges, bodies of ...</td>\n","      <td>Non-GPE</td>\n","      <td>ORG</td>\n","      <td>0.9655</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Non-GPE locations, mountain ranges, bodies of ...</td>\n","      <td>Sahara Destert</td>\n","      <td>LOC</td>\n","      <td>0.5385</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Objects, vehicles, foods, etc. (Not services.)...</td>\n","      <td>Playstation</td>\n","      <td>PRODUCT</td>\n","      <td>0.9356</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Named hurricanes, battles, wars, sports events...</td>\n","      <td>hurricane Katrina</td>\n","      <td>EVENT</td>\n","      <td>0.7472</td>\n","      <td>[[-0.3515700101852417, -0.1662600040435791, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Titles of books, songs, etc. Like the Mona Lisa</td>\n","      <td>Lisa</td>\n","      <td>PERSON</td>\n","      <td>0.6172</td>\n","      <td>[[0.5689799785614014, -0.38422998785972595, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Named documents made into laws. Like the Decla...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[-0.3515700101852417, -0.1662600040435791, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Any named language. Like English</td>\n","      <td>English</td>\n","      <td>LANGUAGE</td>\n","      <td>0.9894</td>\n","      <td>[[-0.2367600053548813, 0.15658999979496002, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Absolute or relative dates or periods. Like ev...</td>\n","      <td>second</td>\n","      <td>ORDINAL</td>\n","      <td>0.2531</td>\n","      <td>[[-0.0853630006313324, -0.5337499976158142, 1....</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Times smaller than a day. Like every minute</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[-0.29739999771118164, 0.1302099972963333, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Percentage, including ”%“. Like 55% of workers...</td>\n","      <td>55%</td>\n","      <td>PERCENT</td>\n","      <td>0.84935</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Monetary values, including unit. Like 50$ for ...</td>\n","      <td>50$</td>\n","      <td>MONEY</td>\n","      <td>0.74255</td>\n","      <td>[[0.3520300090312958, -0.1374099999666214, 0.2...</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Measurements, as of weight or distance. Like t...</td>\n","      <td>50kg</td>\n","      <td>PERSON</td>\n","      <td>0.2981</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>“first”, “second”, etc. Like David place first...</td>\n","      <td>David</td>\n","      <td>PERSON</td>\n","      <td>0.9373</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>“first”, “second”, etc. Like David place first...</td>\n","      <td>first</td>\n","      <td>ORDINAL</td>\n","      <td>0.9203</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Numerals that do not fall under another type. ...</td>\n","      <td>hundreds</td>\n","      <td>CARDINAL</td>\n","      <td>0.9277</td>\n","      <td>[[-0.2671700119972229, 0.7479100227355957, -0....</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Numerals that do not fall under another type. ...</td>\n","      <td>NLU</td>\n","      <td>ORG</td>\n","      <td>0.9371</td>\n","      <td>[[-0.2671700119972229, 0.7479100227355957, -0....</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed21a6d5-9147-49cb-9ec3-974539119971')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-ed21a6d5-9147-49cb-9ec3-974539119971 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-ed21a6d5-9147-49cb-9ec3-974539119971');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"qgGdEUgkMika","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1649992981349,"user_tz":-300,"elapsed":6450,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"ca02131c-8de1-4ff3-a8d3-b78c887ad5f5"},"source":["text = [\"Barclays misled shareholders and the public about one of the biggest investments in the bank's history, a BBC Panorama investigation has found.\",\n","\"The bank announced in 2008 that Manchester City owner Sheikh Mansour had agreed to invest more than £3bn.\",\n","\"But the BBC found that the money, which helped Barclays avoid a bailout by British taxpayers, actually came from the Abu Dhabi government.\",\n","\"Barclays said the mistake in its accounts was 'a drafting error'.\",\n","\"Unlike RBS and Lloyds TSB, Barclays narrowly avoided having to request a government bailout late in 2008 after it was rescued by £7bn worth of new investment, most of which came from the Gulf states of Qatar and Abu Dhabi.\",\n","\"The S&P 500's price to earnings multiple is 71% higher than Apple's, and if Apple were simply valued at the same multiple, its share price would be $840, which is 52% higher than its current price.\",\n","\"Alice has a cat named Alice and also a dog named Alice and also a parrot named Alice, it is her favorite name!\"\n","] + example_text\n","ner_df = nlu.load('ner.onto').predict(text, output_level='chunk')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["onto_recognize_entities_sm download started this may take some time.\n","Approx size to download 160.1 MB\n","[OK!]\n"]}]},{"cell_type":"markdown","metadata":{"id":"STc7iOwtljGo"},"source":["## Lets explore our data which the predicted NER tags and visalize them!    \n","\n","We specify [1:] so we dont se the count for the O-tag wich is the most common, since most words in a sentence are not named entities and thus not part of a chunk"]},{"cell_type":"code","metadata":{"id":"UDSAYjadlfdK","colab":{"base_uri":"https://localhost:8080/","height":391},"executionInfo":{"status":"ok","timestamp":1649993214771,"user_tz":-300,"elapsed":1535,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"3e3da774-7bd6-4bc9-99b9-985e7bac9716"},"source":["ner_df['entities_onto'].value_counts()[1:].plot.bar(title='Occurence of Named Entity tokens in dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f1ed555bf10>"]},"metadata":{},"execution_count":7},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"oRH8yxMcTdv9","executionInfo":{"status":"ok","timestamp":1649993217595,"user_tz":-300,"elapsed":2835,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"58164d26-76ff-455f-d95d-be2f1c326f36"},"source":["ner_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                             document         entities_onto  \\\n","0   Barclays misled shareholders and the public ab...              Barclays   \n","0   Barclays misled shareholders and the public ab...             about one   \n","0   Barclays misled shareholders and the public ab...          BBC Panorama   \n","1   The bank announced in 2008 that Manchester Cit...                  2008   \n","1   The bank announced in 2008 that Manchester Cit...       Manchester City   \n","1   The bank announced in 2008 that Manchester Cit...        Sheikh Mansour   \n","2   But the BBC found that the money, which helped...                   BBC   \n","2   But the BBC found that the money, which helped...              Barclays   \n","2   But the BBC found that the money, which helped...               British   \n","2   But the BBC found that the money, which helped...             Abu Dhabi   \n","3   Barclays said the mistake in its accounts was ...              Barclays   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...                   RBS   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...  Lloyds TSB, Barclays   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...                  2008   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...                  Gulf   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...                 Qatar   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...            Abu Dhabi.   \n","5   The S&P 500's price to earnings multiple is 71...                   S&P   \n","5   The S&P 500's price to earnings multiple is 71...                 500's   \n","5   The S&P 500's price to earnings multiple is 71...                   71%   \n","5   The S&P 500's price to earnings multiple is 71...              Apple's,   \n","5   The S&P 500's price to earnings multiple is 71...                 Apple   \n","5   The S&P 500's price to earnings multiple is 71...                  $840   \n","5   The S&P 500's price to earnings multiple is 71...                   52%   \n","6   Alice has a cat named Alice and also a dog nam...                 Alice   \n","6   Alice has a cat named Alice and also a dog nam...                 Alice   \n","6   Alice has a cat named Alice and also a dog nam...                 Alice   \n","6   Alice has a cat named Alice and also a dog nam...                Alice,   \n","7      People, including fictional like Harry Potter.         Harry Potter.   \n","8   Nationalities or religious or political groups...                   NaN   \n","9   Buildings, airports, highways, bridges, etc. l...      New York Airport   \n","10  Companies, agencies, institutions, etc. like M...             Microsoft   \n","11            Countries, cities, states. like Germany               Germany   \n","12  Non-GPE locations, mountain ranges, bodies of ...               Non-GPE   \n","12  Non-GPE locations, mountain ranges, bodies of ...        Sahara Destert   \n","13  Objects, vehicles, foods, etc. (Not services.)...           Playstation   \n","14  Named hurricanes, battles, wars, sports events...     hurricane Katrina   \n","15    Titles of books, songs, etc. Like the Mona Lisa                  Lisa   \n","16  Named documents made into laws. Like the Decla...                   NaN   \n","17                   Any named language. Like English               English   \n","18  Absolute or relative dates or periods. Like ev...                second   \n","19        Times smaller than a day. Like every minute                   NaN   \n","20  Percentage, including ”%“. Like 55% of workers...                   55%   \n","21  Monetary values, including unit. Like 50$ for ...                   50$   \n","22  Measurements, as of weight or distance. Like t...                  50kg   \n","23  “first”, “second”, etc. Like David place first...                 David   \n","23  “first”, “second”, etc. Like David place first...                 first   \n","24  Numerals that do not fall under another type. ...              hundreds   \n","24  Numerals that do not fall under another type. ...                   NLU   \n","\n","   entities_onto_class entities_onto_confidence  \\\n","0                  ORG                   0.9978   \n","0             CARDINAL                   0.7062   \n","0                  ORG                   0.7376   \n","1                 DATE                   0.7053   \n","1                  GPE                  0.94465   \n","1               PERSON                  0.85805   \n","2                  ORG                      1.0   \n","2                  ORG                   0.9982   \n","2                 NORP                   0.9884   \n","2                  GPE                  0.59695   \n","3                  ORG                   0.9987   \n","4                  ORG                   0.9999   \n","4                  ORG                0.7895333   \n","4                 DATE                   0.8079   \n","4                  LOC                   0.9149   \n","4                  GPE                   0.9996   \n","4                  GPE                   0.5278   \n","5                  ORG                   0.9846   \n","5                 DATE                   0.8317   \n","5              PERCENT                   0.9293   \n","5             CARDINAL                   0.2754   \n","5                  ORG                   0.9992   \n","5             CARDINAL                   0.6073   \n","5              PERCENT               0.96535003   \n","6               PERSON                   0.9969   \n","6               PERSON                   0.9601   \n","6               PERSON                   0.9546   \n","6               PERSON                     0.68   \n","7               PERSON                   0.3747   \n","8                  NaN                      NaN   \n","9                  FAC               0.49666667   \n","10                 ORG                    0.946   \n","11                 GPE                   0.9813   \n","12                 ORG                   0.9655   \n","12                 LOC                   0.5385   \n","13             PRODUCT                   0.9356   \n","14               EVENT                   0.7472   \n","15              PERSON                   0.6172   \n","16                 NaN                      NaN   \n","17            LANGUAGE                   0.9894   \n","18             ORDINAL                   0.2531   \n","19                 NaN                      NaN   \n","20             PERCENT                  0.84935   \n","21               MONEY                  0.74255   \n","22              PERSON                   0.2981   \n","23              PERSON                   0.9373   \n","23             ORDINAL                   0.9203   \n","24            CARDINAL                   0.9277   \n","24                 ORG                   0.9371   \n","\n","                                  word_embedding_onto  \n","0   [[0.044123999774456024, -0.47940999269485474, ...  \n","0   [[0.044123999774456024, -0.47940999269485474, ...  \n","0   [[0.044123999774456024, -0.47940999269485474, ...  \n","1   [[-0.03819400072097778, -0.24487000703811646, ...  \n","1   [[-0.03819400072097778, -0.24487000703811646, ...  \n","1   [[-0.03819400072097778, -0.24487000703811646, ...  \n","2   [[-0.05707800015807152, 0.3987399935722351, 0....  \n","2   [[-0.05707800015807152, 0.3987399935722351, 0....  \n","2   [[-0.05707800015807152, 0.3987399935722351, 0....  \n","2   [[-0.05707800015807152, 0.3987399935722351, 0....  \n","3   [[0.044123999774456024, -0.47940999269485474, ...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","6   [[0.28501999378204346, -0.4355500042438507, 0....  \n","6   [[0.28501999378204346, -0.4355500042438507, 0....  \n","6   [[0.28501999378204346, -0.4355500042438507, 0....  \n","6   [[0.28501999378204346, -0.4355500042438507, 0....  \n","7   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","8   [[-0.02076599933207035, 0.5784800052642822, 0....  \n","9   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","10  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","11  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","12  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","12  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","13  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","14  [[-0.3515700101852417, -0.1662600040435791, 0....  \n","15  [[0.5689799785614014, -0.38422998785972595, 0....  \n","16  [[-0.3515700101852417, -0.1662600040435791, 0....  \n","17  [[-0.2367600053548813, 0.15658999979496002, 0....  \n","18  [[-0.0853630006313324, -0.5337499976158142, 1....  \n","19  [[-0.29739999771118164, 0.1302099972963333, 0....  \n","20  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","21  [[0.3520300090312958, -0.1374099999666214, 0.2...  \n","22  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","23  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","23  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","24  [[-0.2671700119972229, 0.7479100227355957, -0....  \n","24  [[-0.2671700119972229, 0.7479100227355957, -0....  "],"text/html":["\n","  <div id=\"df-b6bf1c1c-3539-4252-a4de-9b6500be5920\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>entities_onto</th>\n","      <th>entities_onto_class</th>\n","      <th>entities_onto_confidence</th>\n","      <th>word_embedding_onto</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Barclays misled shareholders and the public ab...</td>\n","      <td>Barclays</td>\n","      <td>ORG</td>\n","      <td>0.9978</td>\n","      <td>[[0.044123999774456024, -0.47940999269485474, ...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>Barclays misled shareholders and the public ab...</td>\n","      <td>about one</td>\n","      <td>CARDINAL</td>\n","      <td>0.7062</td>\n","      <td>[[0.044123999774456024, -0.47940999269485474, ...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>Barclays misled shareholders and the public ab...</td>\n","      <td>BBC Panorama</td>\n","      <td>ORG</td>\n","      <td>0.7376</td>\n","      <td>[[0.044123999774456024, -0.47940999269485474, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>The bank announced in 2008 that Manchester Cit...</td>\n","      <td>2008</td>\n","      <td>DATE</td>\n","      <td>0.7053</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>The bank announced in 2008 that Manchester Cit...</td>\n","      <td>Manchester City</td>\n","      <td>GPE</td>\n","      <td>0.94465</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>The bank announced in 2008 that Manchester Cit...</td>\n","      <td>Sheikh Mansour</td>\n","      <td>PERSON</td>\n","      <td>0.85805</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>But the BBC found that the money, which helped...</td>\n","      <td>BBC</td>\n","      <td>ORG</td>\n","      <td>1.0</td>\n","      <td>[[-0.05707800015807152, 0.3987399935722351, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>But the BBC found that the money, which helped...</td>\n","      <td>Barclays</td>\n","      <td>ORG</td>\n","      <td>0.9982</td>\n","      <td>[[-0.05707800015807152, 0.3987399935722351, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>But the BBC found that the money, which helped...</td>\n","      <td>British</td>\n","      <td>NORP</td>\n","      <td>0.9884</td>\n","      <td>[[-0.05707800015807152, 0.3987399935722351, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>But the BBC found that the money, which helped...</td>\n","      <td>Abu Dhabi</td>\n","      <td>GPE</td>\n","      <td>0.59695</td>\n","      <td>[[-0.05707800015807152, 0.3987399935722351, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Barclays said the mistake in its accounts was ...</td>\n","      <td>Barclays</td>\n","      <td>ORG</td>\n","      <td>0.9987</td>\n","      <td>[[0.044123999774456024, -0.47940999269485474, ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>RBS</td>\n","      <td>ORG</td>\n","      <td>0.9999</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>Lloyds TSB, Barclays</td>\n","      <td>ORG</td>\n","      <td>0.7895333</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>2008</td>\n","      <td>DATE</td>\n","      <td>0.8079</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>Gulf</td>\n","      <td>LOC</td>\n","      <td>0.9149</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>Qatar</td>\n","      <td>GPE</td>\n","      <td>0.9996</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>Abu Dhabi.</td>\n","      <td>GPE</td>\n","      <td>0.5278</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>S&amp;P</td>\n","      <td>ORG</td>\n","      <td>0.9846</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>500's</td>\n","      <td>DATE</td>\n","      <td>0.8317</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>71%</td>\n","      <td>PERCENT</td>\n","      <td>0.9293</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>Apple's,</td>\n","      <td>CARDINAL</td>\n","      <td>0.2754</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>Apple</td>\n","      <td>ORG</td>\n","      <td>0.9992</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>$840</td>\n","      <td>CARDINAL</td>\n","      <td>0.6073</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>52%</td>\n","      <td>PERCENT</td>\n","      <td>0.96535003</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Alice has a cat named Alice and also a dog nam...</td>\n","      <td>Alice</td>\n","      <td>PERSON</td>\n","      <td>0.9969</td>\n","      <td>[[0.28501999378204346, -0.4355500042438507, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Alice has a cat named Alice and also a dog nam...</td>\n","      <td>Alice</td>\n","      <td>PERSON</td>\n","      <td>0.9601</td>\n","      <td>[[0.28501999378204346, -0.4355500042438507, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Alice has a cat named Alice and also a dog nam...</td>\n","      <td>Alice</td>\n","      <td>PERSON</td>\n","      <td>0.9546</td>\n","      <td>[[0.28501999378204346, -0.4355500042438507, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Alice has a cat named Alice and also a dog nam...</td>\n","      <td>Alice,</td>\n","      <td>PERSON</td>\n","      <td>0.68</td>\n","      <td>[[0.28501999378204346, -0.4355500042438507, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>People, including fictional like Harry Potter.</td>\n","      <td>Harry Potter.</td>\n","      <td>PERSON</td>\n","      <td>0.3747</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Nationalities or religious or political groups...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[-0.02076599933207035, 0.5784800052642822, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Buildings, airports, highways, bridges, etc. l...</td>\n","      <td>New York Airport</td>\n","      <td>FAC</td>\n","      <td>0.49666667</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Companies, agencies, institutions, etc. like M...</td>\n","      <td>Microsoft</td>\n","      <td>ORG</td>\n","      <td>0.946</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Countries, cities, states. like Germany</td>\n","      <td>Germany</td>\n","      <td>GPE</td>\n","      <td>0.9813</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Non-GPE locations, mountain ranges, bodies of ...</td>\n","      <td>Non-GPE</td>\n","      <td>ORG</td>\n","      <td>0.9655</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Non-GPE locations, mountain ranges, bodies of ...</td>\n","      <td>Sahara Destert</td>\n","      <td>LOC</td>\n","      <td>0.5385</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Objects, vehicles, foods, etc. (Not services.)...</td>\n","      <td>Playstation</td>\n","      <td>PRODUCT</td>\n","      <td>0.9356</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Named hurricanes, battles, wars, sports events...</td>\n","      <td>hurricane Katrina</td>\n","      <td>EVENT</td>\n","      <td>0.7472</td>\n","      <td>[[-0.3515700101852417, -0.1662600040435791, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Titles of books, songs, etc. Like the Mona Lisa</td>\n","      <td>Lisa</td>\n","      <td>PERSON</td>\n","      <td>0.6172</td>\n","      <td>[[0.5689799785614014, -0.38422998785972595, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Named documents made into laws. Like the Decla...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[-0.3515700101852417, -0.1662600040435791, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Any named language. Like English</td>\n","      <td>English</td>\n","      <td>LANGUAGE</td>\n","      <td>0.9894</td>\n","      <td>[[-0.2367600053548813, 0.15658999979496002, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Absolute or relative dates or periods. Like ev...</td>\n","      <td>second</td>\n","      <td>ORDINAL</td>\n","      <td>0.2531</td>\n","      <td>[[-0.0853630006313324, -0.5337499976158142, 1....</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Times smaller than a day. Like every minute</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[-0.29739999771118164, 0.1302099972963333, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>Percentage, including ”%“. Like 55% of workers...</td>\n","      <td>55%</td>\n","      <td>PERCENT</td>\n","      <td>0.84935</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>Monetary values, including unit. Like 50$ for ...</td>\n","      <td>50$</td>\n","      <td>MONEY</td>\n","      <td>0.74255</td>\n","      <td>[[0.3520300090312958, -0.1374099999666214, 0.2...</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>Measurements, as of weight or distance. Like t...</td>\n","      <td>50kg</td>\n","      <td>PERSON</td>\n","      <td>0.2981</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>“first”, “second”, etc. Like David place first...</td>\n","      <td>David</td>\n","      <td>PERSON</td>\n","      <td>0.9373</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>“first”, “second”, etc. Like David place first...</td>\n","      <td>first</td>\n","      <td>ORDINAL</td>\n","      <td>0.9203</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>Numerals that do not fall under another type. ...</td>\n","      <td>hundreds</td>\n","      <td>CARDINAL</td>\n","      <td>0.9277</td>\n","      <td>[[-0.2671700119972229, 0.7479100227355957, -0....</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>Numerals that do not fall under another type. ...</td>\n","      <td>NLU</td>\n","      <td>ORG</td>\n","      <td>0.9371</td>\n","      <td>[[-0.2671700119972229, 0.7479100227355957, -0....</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6bf1c1c-3539-4252-a4de-9b6500be5920')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-b6bf1c1c-3539-4252-a4de-9b6500be5920 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-b6bf1c1c-3539-4252-a4de-9b6500be5920');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"rlcEvP9tOSiy","colab":{"base_uri":"https://localhost:8080/","height":391},"executionInfo":{"status":"ok","timestamp":1649993218134,"user_tz":-300,"elapsed":559,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"1ffddd65-e1be-497a-df9f-5a4803f8c034"},"source":["ner_type_to_viz = 'ORG'\n","ner_df[ner_df.entities_onto_class == ner_type_to_viz]['entities_onto'].value_counts().plot.bar(title='Most often occuring ORG labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f1ed3f4b9d0>"]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"ks6NDXg7RXG3","colab":{"base_uri":"https://localhost:8080/","height":364},"executionInfo":{"status":"ok","timestamp":1649993218698,"user_tz":-300,"elapsed":572,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"d2336f4a-44e6-468d-f26d-b42737a85f9b"},"source":["ner_type_to_viz = 'LOC'\n","ner_df[ner_df.entities_onto_class == ner_type_to_viz]['entities_onto'].value_counts().plot.bar(title='Most often occuring LOC labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f1ed3a219d0>"]},"metadata":{},"execution_count":10},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[""],"metadata":{"id":"s8gRw2rYQmD_"},"execution_count":null,"outputs":[]}]}